Rebalancing data storage in a dispersed storage network

ABSTRACT

A method for execution by a dispersed storage and task (DST) execution unit includes generating location weight data that includes a plurality of location weights assigned to a plurality of memory devices of the DST execution unit. A first one of the plurality of memory devices and a second one of the plurality of memory devices are selected for reallocation based on the location weight data. The reallocation is executed by removing a data slice from the first one of the plurality of memory devices and storing the data slice in the second one of the plurality of memory devices.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. §120 as a continuation-in-part of U.S. Utility applicationSer. No. 15/058,408, entitled “ACCESSING COMMON DATA IN A DISPERSEDSTORAGE NETWORK”, filed Mar. 2, 2016, which claims priority pursuant to35 U.S.C. §119(e) to U.S. Provisional Application No. 62/154,886,entitled “BALANCING MAINTENANCE AND ACCESS TASKS IN A DISPERSED STORAGENETWORK”, filed Apr. 30, 2015, both of which are hereby incorporatedherein by reference in their entirety and made part of the present U.S.Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 10 is a schematic block diagram of an embodiment of a decentralizedagreement module in accordance with the present invention; and

FIG. 11 is a logic diagram of an example of a method of rebalancing datastorage in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a dispersed storage and task (DST) execution unit and aset of storage units may be interchangeably referred to as a set of DSTexecution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each managing unit 18 and the integrity processing unit 20 maybe separate computing devices, may be a common computing device, and/ormay be integrated into one or more of the computing devices 12-16 and/orinto one or more of the storage units 36. In various embodiments,computing devices 12-16 can include user devices and/or can be utilizedby a requesting entity generating access requests, which can includerequests to read or write data to storage units in the DSN.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one 10 device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm(IDA), Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1 throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1−T), a data segment number (e.g., oneof 1−Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes a distributed storage and task (DST)processing unit 916, which can be utilized by implementing computingdevice 16 of FIG. 1, for example functioning as a DST processing agentfor computing device 14 as described previously. FIG. 9 also includesthe network 24 of FIG. 1, and a set of DST execution (EX) units 1-n,which can each be implemented by utilizing a storage unit 36 of FIG. 1.Each DST execution unit can include a processing module 84 and aplurality of memories (e.g., memories 1-10), for example, by utilizingthe computing core 26 of FIG. 2. The DSN functions to store an encodeddata slice in one of a plurality of memory devices (e.g., the memories1-10 in each DST execution unit 1-n) in accordance with memory devicepriorities.

In an example of operation of the storing of the encoded data slice, foreach memory section of a plurality of memory sections of the pluralityof memory devices, a storage unit can determine a location weight basedon one or more of a storage utilization level of each of the memorydevices, an availability level of each of the memory devices, and/orsystem registry information, where memory capacity of the plurality ofmemory devices is divided into the plurality of memory sections tofacilitate migration of stored encoded data slices in a resection bymemory section approach. In an instance of establishing the plurality ofmemory sections, the processing module 84 of each DST execution unit canestablish a number of the plurality of memory sections to be, forexample, 40,000 when each of the 10 memory devices includes 4 TB ofcapacity and a desired memory section to enable efficient migration ofencoded data slices is 1 GB (e.g., 4000 memory sections per memorydevice). As such, a mapping is established from each of the memorysections to each of the member devices (e.g., memory sections 0001through 4,000 map to memory device 1, etc.)

In an example of determining the location weights, the processing module84 can establish a higher weight for memory devices associated withhigher storage capacity levels (e.g., a new device). As another example,the processing module 84 establishes a lower weight when the memorydevice is associated with a storage capacity utilization level that isabove a utilization threshold level (e.g., an almost full memorydevice). As yet another example, the processing module establishes azero weight when the memory device has failed. In various embodiments, afirst location weight compares favorably to a second location weight ifit is greater than the second location weight and/or if it shouldreceive greater priority in assignment of new or migrating encodedslices.

Having determined the location weights, when detecting that at least onestored encoded data slices stored improperly (e.g., in a memory sectionthat assigned to the encoded data slice), the processing module 84 canfacilitates migration of the at least one encoded data slice to a memorydevice associated with the encoded data slice in accordance with andecentralized agreement protocol (DAP) function utilizing the determinedlocation weights of the plurality of memory device sections. Forexample, the processing module 84, for each memory section, performs thedecentralized agreement protocol function on a slice name of the encodeddata slice utilizing the location weights to produce a correspondingscore, identifies a memory section associated with the slice name (e.g.,associated with a highest score of all the scores of all the memorysections), identifies a memory device associated with the memory section(e.g., a mapping based on the establishing of the plurality of memorysections for the plurality of memory devices), and facilitates transferof the encoded data slice (e.g., a migration slice) to the identifiedmemory device.

When receiving a slice access message, a storage unit can identify amemory device associated with the slice access request utilizing thedecentralized agreement protocol function and the location weights ofthe plurality of memory sections of the storage unit. For example, theprocessing module 84 receives, via the network 24, an access message 1from the DST processing unit 916 and applies the decentralized agreementprotocol function to a slice name of the access message 1 utilizing thelocation weights of each of the 40,000 memory sections to produce 40,000scores, identifies a highest score, identifies a memory sectionassociated with the highest score, and identifies a memory deviceassociated with the memory section in accordance with the mapping.Having identified the memory device, the storage unit utilizes theidentified memory device to process the slice access message. Forexample, when storing data, the processing module 84 stories a receivedencoded data slice in the identified memory device. As another example,when retrieving data, the processing module 84 retrieves a storedencoded data slice from the identified memory device and sends theretrieved encoded data slice to a requesting entity (e.g., the DSTprocessing unit 916).

In various embodiments, a processing system of a dispersed storage andtask (DST) execution unit includes at least one processor and a memorythat stores operational instructions, that when executed by the at leastone processor cause the processing system to generate location weightdata that includes a plurality of location weights assigned to aplurality of memory devices of the DST execution unit. A first one ofthe plurality of memory devices and a second one of the plurality ofmemory devices are selected for reallocation based on the locationweight data. The reallocation is executed by removing a data slice fromthe first one of the plurality of memory devices and storing the dataslice in the second one of the plurality of memory devices.

In various embodiments, the plurality of location weights are assignedbased on storage utilization levels of the plurality of memory devicesand/or storage availability levels of the plurality of memory devices.In various embodiments, the first one of the plurality of memory devicesand the second one of the plurality of memory devices are selected inresponse to a second location weight corresponding to the second one ofthe plurality of memory devices comparing favorably to a first locationweight corresponding to the first one of the plurality of memorydevices. In various embodiments, the second location weight comparesfavorably to the first location weight in response to the second one ofthe plurality of memory devices having a higher storage availabilitylevel than the first one of the plurality of memory devices. In variousembodiments, selecting the first one of the plurality of memory devicesand the second one of the plurality of memory devices is further basedon a decentralized agreement protocol.

In various embodiments, the first one of the plurality of memory devicesincludes a plurality of memory sections, wherein the location weightdata further includes a plurality of section weights corresponding tothe plurality of memory sections, and wherein the data slice is selectedbased on the plurality of section weights. In various embodiments, thesecond one of the plurality of memory devices includes a plurality ofmemory sections, wherein the location weight data further includes aplurality of section weights corresponding to the plurality of memorysections, wherein one of the plurality of memory sections is selectedbased on the plurality of section weights, and wherein the data slice isstored in the selected one of the plurality of memory sections.

In various embodiments, a second data slice is received for storage viaa network. A third one of the plurality of memory devices is selectedbased on the location weight data, and the second data slice is storedin the third one the plurality of memory devices. In variousembodiments, an access request is received via a network that includesan identifier corresponding to the data slice. Slice retrieval data isgenerated by determining the data slice is stored in the second one ofthe plurality of memory devices based on the identifier and the locationweight data. The data slice is retrieved from the second one of theplurality of memory devices based on the slice retrieval data, and anaccess response that includes the data slice is generated fortransmission via the network. In various embodiments, the sliceretrieval data is further generated based on a decentralized agreementprotocol.

FIG. 10 is a schematic block diagram of an embodiment of a decentralizedagreement module that includes a set of deterministic functions 1−N, aset of normalizing functions 1−N, a set of scoring functions 1−N, and aranking function. Each of the deterministic function, the normalizingfunction, the scoring function, and the ranking function, may beimplemented utilizing the processing module 84 of FIG. 9. Thedecentralized agreement module may be implemented utilizing any moduleand/or unit of a dispersed storage network (DSN). For example, thedecentralized agreement module is implemented utilizing the distributedstorage and task (DST) client module 34 of FIG. 1.

The decentralized agreement module functions to receive a ranked scoringinformation request and to generate ranked scoring information based onthe ranked scoring information request and other information. The rankedscoring information request includes one or more of an asset identifier(ID) of an asset associated with the request, an asset type indicator,one or more location identifiers of locations associated with the DSN,one or more corresponding location weights, and a requesting entity ID.The asset includes any portion of data associated with the DSN includingone or more asset types including a data object, a data record, anencoded data slice, a data segment, a set of encoded data slices, and aplurality of sets of encoded data slices. As such, the asset ID of theasset includes one or more of a data name, a data record identifier, asource name, a slice name, and a plurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations includes one or more of a storage unit, a memory device ofthe storage unit, a site, a storage pool of storage units, a pillarindex associated with each encoded data slice of a set of encoded dataslices generated by an information dispersal algorithm (IDA), a DSclient module 34 of FIG. 1, a computing device 16 of FIG. 1, anintegrity processing unit 20 of FIG. 1, a managing unit 18 of FIG. 1, acomputing device 12 of FIG. 1, and a computing device 14 of FIG. 1.

Each location is associated with a location weight based on one or moreof a resource prioritization of utilization scheme and physicalconfiguration of the DSN. The location weight includes an arbitrary biaswhich adjusts a proportion of selections to an associated location suchthat a probability that an asset will be mapped to that location isequal to the location weight divided by a sum of all location weightsfor all locations of comparison. For example, each storage pool of aplurality of storage pools is associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacityare associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information includes location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule to produce ranked scoring information with regards to selectionof a memory device of the set of memory devices for accessing aparticular encoded data slice (e.g., where the asset ID includes a slicename of the particular encoded data slice).

The decentralized agreement module outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module andthe second requesting entity identifies the first storage pool of theplurality of storage pools for retrieving the set of encoded data slicesutilizing the decentralized agreement module.

In an example of operation, the decentralized agreement module receivesthe ranked scoring information request. Each deterministic functionperforms a deterministic function on a combination and/or concatenation(e.g., add, append, interleave) of the asset ID of the request and anassociated location ID of the set of location IDs to produce an interimresult. The deterministic function includes at least one of a hashingfunction, a hash-based message authentication code function, a maskgenerating function, a cyclic redundancy code function, hashing moduleof a number of locations, consistent hashing, rendezvous hashing, and asponge function. As a specific example, deterministic function 2 appendsa location ID 2 of a storage pool 2 to a source name as the asset ID toproduce a combined value and performs the mask generating function onthe combined value to produce interim result 2.

With a set of interim results 1−N, each normalizing function performs anormalizing function on a corresponding interim result to produce acorresponding normalized interim result. The performing of thenormalizing function includes dividing the interim result by a number ofpossible permutations of the output of the deterministic function toproduce the normalized interim result. For example, normalizing function2 performs the normalizing function on the interim result 2 to produce anormalized interim result 2.

With a set of normalized interim results 1−N, each scoring functionperforms a scoring function on a corresponding normalized interim resultto produce a corresponding score. The performing of the scoring functionincludes dividing an associated location weight by a negative log of thenormalized interim result. For example, scoring function 2 divideslocation weight 2 of the storage pool 2 (e.g., associated with locationID 2) by a negative log of the normalized interim result 2 to produce ascore 2.

With a set of scores 1−N, the ranking function performs a rankingfunction on the set of scores 1−N to generate the ranked scoringinformation. The ranking function includes rank ordering each score withother scores of the set of scores 1−N, where a highest score is rankedfirst. As such, a location associated with the highest score may beconsidered a highest priority location for resource utilization (e.g.,accessing, storing, retrieving, etc., the given asset of the request).Having generated the ranked scoring information, the decentralizedagreement module outputs the ranked scoring information to therequesting entity.

FIG. 11 is a flowchart illustrating an example of rebalancing datastorage. In particular, a method is presented for use in associationwith one or more functions and features described in conjunction withFIGS. 1-10, for execution by a dispersed storage and task (DST)execution unit that includes a processor or via another processingsystem of a dispersed storage network that includes at least oneprocessor and memory that stores instruction that configure theprocessor or processors to perform the steps described below. Step 1102generating location weight data that includes a plurality of locationweights assigned to a plurality of memory devices of the DST executionunit. Step 1104 includes selecting a first one of the plurality ofmemory devices and a second one of the plurality of memory devices forreallocation based on the location weight data. Step 1106 includesexecuting the reallocation by removing a data slice from the first oneof the plurality of memory devices and storing the data slice in thesecond one of the plurality of memory devices.

In various embodiments, the plurality of location weights are assignedbased on storage utilization levels of the plurality of memory devicesand/or storage availability levels of the plurality of memory devices.In various embodiments, the first one of the plurality of memory devicesand the second one of the plurality of memory devices are selected inresponse to a second location weight corresponding to the second one ofthe plurality of memory devices comparing favorably to a first locationweight corresponding to the first one of the plurality of memorydevices. In various embodiments, the second location weight comparesfavorably to the first location weight in response to the second one ofthe plurality of memory devices having a higher storage availabilitylevel than the first one of the plurality of memory devices. In variousembodiments, selecting the first one of the plurality of memory devicesand the second one of the plurality of memory devices is further basedon a decentralized agreement protocol.

In various embodiments, the first one of the plurality of memory devicesincludes a plurality of memory sections, wherein the location weightdata further includes a plurality of section weights corresponding tothe plurality of memory sections, and wherein the data slice is selectedbased on the plurality of section weights. In various embodiments, thesecond one of the plurality of memory devices includes a plurality ofmemory sections, wherein the location weight data further includes aplurality of section weights corresponding to the plurality of memorysections, wherein one of the plurality of memory sections is selectedbased on the plurality of section weights, and wherein the data slice isstored in the selected one of the plurality of memory sections.

In various embodiments, a second data slice is received for storage viaa network. A third one of the plurality of memory devices is selectedbased on the location weight data, and the second data slice is storedin the third one the plurality of memory devices. In variousembodiments, an access request is received via a network that includesan identifier corresponding to the data slice. Slice retrieval data isgenerated by determining the data slice is stored in the second one ofthe plurality of memory devices based on the identifier and the locationweight data. The data slice is retrieved from the second one of theplurality of memory devices based on the slice retrieval data, and anaccess response that includes the data slice is generated fortransmission via the network. In various embodiments, the sliceretrieval data is further generated based on a decentralized agreementprotocol.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to generate location weight data that includesassigning a plurality of location weights to a plurality of memorydevices of the DST execution unit. A first one of the plurality ofmemory devices and a second one of the plurality of memory devices areselected for reallocation based on the location weight data. Thereallocation is executed by removing a data slice from the first one ofthe plurality of memory devices and storing the data slice in the secondone of the plurality of memory devices.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a dispersed storage andtask (DST) execution unit that includes a processor, the methodcomprises: generating location weight data that includes a plurality oflocation weights assigned to a plurality of memory devices of the DSTexecution unit; selecting a first one of the plurality of memory devicesand a second one of the plurality of memory devices for reallocationbased on the location weight data; and executing the reallocation byremoving a data slice from the first one of the plurality of memorydevices and storing the data slice in the second one of the plurality ofmemory devices.
 2. The method of claim 1, wherein the plurality oflocation weights are assigned based on at least one of: storageutilization levels of the plurality of memory devices or storageavailability levels of the plurality of memory devices.
 3. The method ofclaim 1, wherein the first one of the plurality of memory devices andthe second one of the plurality of memory devices are selected inresponse to a second location weight corresponding to the second one ofthe plurality of memory devices comparing favorably to a first locationweight corresponding to the first one of the plurality of memorydevices.
 4. The method of claim 3, wherein the second location weightcompares favorably to the first location weight in response to thesecond one of the plurality of memory devices having a higher storageavailability level than the first one of the plurality of memorydevices.
 5. The method of claim 1, wherein the first one of theplurality of memory devices includes a plurality of memory sections,wherein the location weight data further includes a plurality of sectionweights corresponding to the plurality of memory sections, and whereinthe data slice is selected based on the plurality of section weights. 6.The method of claim 1, wherein the second one of the plurality of memorydevices includes a plurality of memory sections, wherein the locationweight data further includes a plurality of section weightscorresponding to the plurality of memory sections, wherein one of theplurality of memory sections is selected based on the plurality ofsection weights, and wherein the data slice is stored in the selectedone of the plurality of memory sections.
 7. The method of claim 1,wherein selecting the first one of the plurality of memory devices andthe second one of the plurality of memory devices is further based on adecentralized agreement protocol.
 8. The method of claim 1, furthercomprising: receiving a second data slice for storage via a network;selecting a third one of the plurality of memory devices based on thelocation weight data; and storing the second data slice in the third onethe plurality of memory devices.
 9. The method of claim 1, furthercomprising: receiving an access request via a network that includes anidentifier corresponding to the data slice; generating slice retrievaldata by determining the data slice is stored in the second one of theplurality of memory devices based on the identifier and the locationweight data; retrieving the data slice from the second one of theplurality of memory devices based on the slice retrieval data; andgenerating an access response that includes the data slice fortransmission via the network.
 10. The method of claim 9, wherein theslice retrieval data is further generated based on a decentralizedagreement protocol.
 11. A processing system of a dispersed storage andtask (DST) execution unit comprises: at least one processor; a memorythat stores operational instructions, that when executed by the at leastone processor cause the processing system to: generate location weightdata that includes a plurality of location weights assigned to aplurality of memory devices of the DST execution unit; select a firstone of the plurality of memory devices and a second one of the pluralityof memory devices for reallocation based on the location weight data;and execute the reallocation by removing a data slice from the first oneof the plurality of memory devices and storing the data slice in thesecond one of the plurality of memory devices.
 12. The processing systemof claim 11, wherein the plurality of location weights are assignedbased on at least one of: storage utilization levels of the plurality ofmemory devices or storage availability levels of the plurality of memorydevices.
 13. The processing system of claim 11, wherein the first one ofthe plurality of memory devices and the second one of the plurality ofmemory devices are selected in response to a second location weightcorresponding to the second one of the plurality of memory devicescomparing favorably to a first location weight corresponding to thefirst one of the plurality of memory devices.
 14. The processing systemof claim 13, wherein the second location weight compares favorably tothe first location weight in response to the second one of the pluralityof memory devices having a higher storage availability level than thefirst one of the plurality of memory devices.
 15. The processing systemof claim 11, wherein the first one of the plurality of memory devicesincludes a plurality of memory sections, wherein the location weightdata further includes a plurality of section weights corresponding tothe plurality of memory sections, and wherein the data slice is selectedbased on the plurality of section weights.
 16. The processing system ofclaim 11, wherein the second one of the plurality of memory devicesincludes a plurality of memory sections, wherein the location weightdata further includes a plurality of section weights corresponding tothe plurality of memory sections, wherein one of the plurality of memorysections is selected based on the plurality of section weights, andwherein the data slice is stored in the selected one of the plurality ofmemory sections.
 17. The processing system of claim 11, whereinselecting the first one of the plurality of memory devices and thesecond one of the plurality of memory devices is further based on adecentralized agreement protocol.
 18. The processing system of claim 11,wherein the operational instructions, when executed by the at least oneprocessor, further cause the processor to: receive a second data slicefor storage via a network; select a third one of the plurality of memorydevices based on the location weight data; and store the second dataslice in the third one the plurality of memory devices.
 19. Theprocessing system of claim 11, wherein the operational instructions,when executed by the at least one processor, further cause the processorto: receive an access request via a network that includes an identifiercorresponding to the data slice; generate slice retrieval data bydetermining the data slice is stored in the second one of the pluralityof memory devices based on the identifier and the location weight data;retrieve the data slice from the second one of the plurality of memorydevices based on the slice retrieval data; and generate an accessresponse that includes the data slice for transmission via the network.20. A non-transitory computer readable storage medium comprises: atleast one memory section that stores operational instructions that, whenexecuted by a processing system of a dispersed storage network (DSN)that includes a processor and a memory, causes the processing system to:generate location weight data that includes a plurality of locationweights assigned to a plurality of memory devices; select a first one ofthe plurality of memory devices and a second one of the plurality ofmemory devices for reallocation based on the location weight data; andexecute the reallocation by removing a data slice from the first one ofthe plurality of memory devices and storing the data slice in the secondone of the plurality of memory devices.